Recently, adversarial learning-based methods exert a tremendous fascination to bridge the gap between the source domain and the target domain. GAN [1]} is motivated by the idea of two-play game in game theory. Adversarial training is the process in which the generator and the discriminator compete against each other.
Adversarial learning is firstly applied to domain adaptation in [2]}. Its core idea is to adopt the discriminator to learn domain invariant features. A more general framework is proposed by [3]} for adversarial domain adaptation. The author of [4]} is motivated by [5]} and proposes to align category labels by using the joint distribution of features and predictions.
Inspired by [6]}, [7]} applies the Wasserstein GAN measurement to domain adaptation, and proposes Wasserstein distance guided representation learning.
